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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Vivekanandhan, V. Al-Farouni, Mohammed Shreyas, A.V. Kumar, Gotte Ranjith Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA). © 2024 IEEE.

关键词： Convolutional neural networks

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Unsupervised Multi-Source Domains Adaptation with Locally Topological Persevering

Unsupervised Multi-Source Domains Adaptation with Locally To...

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2023 China Automation Congress, CAC 2023

作者： Chen, Zhifeng Wang, Lvda Li, Ying Ying, Shihui College of Science Shanghai University Department of Mathematics China Beijing Institute of Computer Technology and Applications China College of Computer Engineering and Science Shanghai University China

ISBN: (纸本)9798350303759

Most existing unsupervised domain adaptation (UDA) methods primarily focus on single-source and single-target domain scenarios, referred to as single-source unsupervised domain adaptation. However, in practical applications, it is often possible to collect labeled data from multiple distinct source domains, which not only differ from the target domain but also exhibit dissimilarities among themselves. As a result, the concept of multi-source unsupervised domain adaptation has emerged. While mainstream UDA techniques aim to align features between two domains, addressing domain shift solely through feature alignment might prove inadequate, potentially undermining the inherent structure of the data. To mitigate this risk, this paper introduces a method called Locally Topology Preserving Multi-Source Unsupervised Domain Adaptation (LTP- MDA). By performing clustering on both source and target data, distinct clusters are formed. During the domain-adaptive feature transformation, this method ensures the preservation of the structural integrity of these clusters throughout the transformation. Additionally, due to the possibility of misclassification during the clustering process, we leverage consistency weights derived from genuine labels in the source domain and pseudo-labels in the target domain to guide the clustering process. Experimental results demonstrate the remarkable performance of our approach across various cross-domain adaptation tasks. We extensively evaluate our method on three benchmark datasets, validating its effectiveness and robustness. © 2023 IEEE.

关键词： Topology

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IoT Based Smart Home Using Virtual Key 1

IoT Based Smart Home Using Virtual Key

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Edna Elizabeth, N. Jeffrey Isaac, S. Janit, Sophia Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Department of Electronics and Communication Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367003

In this comprehensive documentation, we embark on an enlightening journey into the dynamic realm of Internet of Things (IoT) home automation. From the seamless integration of smart devices to the complex orchestration of automated processes, IoT home automation redefines the very essence of domesticity. We explore the landscape of cutting-edge advancements, delving into the intricate interplay of sensors, actuators and connectivity protocols that underpin the functionality of smart homes. Furthermore, we delve into the practical applications of IoT in various aspects of home management. This includes optimizing the energy efficiency, monitoring the environment, controlling personalized comfort and enhancing security measures. As we navigate through real-world case studies and exemplary implementations, we gain invaluable insights into the practical challenges and opportunities inherent in deploying IoT solutions within residential settings. In our proposed work, we have developed a prototype for a smart IoT-based smart home to control and monitor various systems such as light, lamp, socket and fan using ESP32 controller. The various controls are performed through relays. The data are sent to the firebase cloud through Wi-Fi and stored in the cloud. Later, the appliances are switched ON/OFF through virtual key using mobile APP. By using virtual keys, we address pertinent ethical and privacy considerations, shedding light on the importance of responsible data stewardship and robust cybersecurity measures. These are crucial in safeguarding the integrity of smart home ecosystems. By combining theoretical frameworks with empirical observations, we aim to provide a comprehensive understanding of IoT home automation. © 2024 IEEE.

关键词： Smart homes

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Empirical Evaluation of Microservices Architecture 1

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Kaushik, Neha Kumar, Harish Raj, Vinay Department of Computer Engineering JC Bose University of Science and Technology Faridabad India Department of Computer Applications National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (数字)9789819923229

ISBN: (纸本)9789819923212

Microservices architecture has gained a lot of interest in recent times in designing large enterprise applications with coupling and scalability as its significant features. Microservices architecture is an architectural approach in which large applications are split into small, loosely coupled services that communicate with one another using lightweight protocols such as RESTful APIs. The identified microservices must be monitored and assessed in order to determine the areas that need to be improved. Therefore, it is required to study the tools and metrics that can help practitioners and developers to continuously evaluate the quality of microservice-based architectures and provide suggestions for refactoring them. This work adapted the empirically based architecture evaluation (EBAE) method for evaluating the microservices-based applications. A web-based application is chosen as a case study and metrics related to QoS attributes are considered. The results obtained are presented, and it is clear from the results that microservices architecture can be strongly recommended for the design of large enterprise applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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Securing the Cyber Supply Chain: A Risk-based Approach to Threat Assessment and Mitigation 4

Securing the Cyber Supply Chain: A Risk-based Approach to Th...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Phani Prathyusha, J.R.V.S.L.P. Esther Jyothi, V. Jhansi, Vadlamudi Sampreeth Chowdary, N. Madhuri, A. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350300093

The system of Cyber Supply Chain (CSC) is characterized by its complexity, consisting of several subsystems, each responsible for a distinct set of responsibilities. Securing the supply chain presents a challenge due to the presence of vulnerabilities and threats throughout the system that has the potential to be taken advantage of at any time, considering that any component of the system is susceptible to such attacks. As a result, supply chain security is difficult to achieve. This has the potential to create a significant interruption to the overall continuity of the company. Therefore, it is of the utmost importance to identify the hazards and make educated guesses about their likely outcomes so that organizations can take the appropriate precautions to ensure the safety of their supply chains. By leveraging a range of factors, such as the expertise and incentives of threat actors, Tactics, Techniques, and Procedures (TT and P), as well as Indicators of Compromise (IoC), the analysis of Cyber Threat Intelligence (CTI) offers valuable information on both identified ansignd unidentified cybersecurity threats. In order to increase the safety of the cyber supply chain, the purpose of this article is to investigate and speculate on potential dangers. The CTI and Machine Learning (ML) approaches have been employed by us in order to study and forecast the risks based on the CTI attributes. This makes it possible to detect the inherent CSC vulnerabilities, which enables suitable control. To enhance the overall security of computer systems, it is imperative to implement specific actions, including the collection of CTI data and the adoption of various machine learning techniques. These techniques encompass Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, which are employed in analyzing the Microsoft Malware Prediction dataset to create predictive analytics. This is done in order to illustrate t

关键词： Supply chains

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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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A Pattern Recognition Model: Hand Gestures Recognition using Convolutional Neural Networks

A Pattern Recognition Model: Hand Gestures Recognition using...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Ayyappa Reddy, D. Esther Jyothi, V. Kasi Viswanath, J. Sampreeth Chowdary, N. Swapna, D. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350333602

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven't acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes. © 2023 IEEE.

关键词： Gesture recognition

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Robustness Challenges in Deep Learning: Strategies for Enhancing Model Resilience 2

Robustness Challenges in Deep Learning: Strategies for Enhan...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Joshi, Prakash Shaikh, M.Z. Varshney, Neeraj Dwivedy, Bhoopendra Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Gla University Department of Computer Engineering and Applications UP Mathura India Galgotias University Department of School of Computer Science & Engineering UP Greater Noida India

ISBN: (纸本)9798350371055

Deep knowledge is the main way the improvement of gadgets, gaining knowledge of generation for more than a few applications from facial popularity to self-reliant vehicles. Deep learning fashions have carried out outstanding successes, even outperforming people in complex obligations. However, robustness demanding situations of deep getting-to-know systems remain an essential difficulty. Deep studying fashions are at risk of malicious inputs that could result in misclassifications or denial-of-provider attacks. Robustness towards entering noise, in addition to robust extraction of beneficial capabilities from noisy datasets, are just a number of the main challenges for deep mastering systems. Current research has discovered a plethora of attack vectors that can be deployed against deep getting-to-know fashions along with 'adversarial' assaults, which contain manipulating the enter records in a particular manner to purpose models to misclassify items. Moreover, dataset bias and absence of right validation can cause structures that are not sturdy towards facts from a domain extraordinary from the facts used for education. A good way to enhance the robustness of deep learning models, researchers are exploring solutions that include countermeasures, specialized architectures, and higher education and checking out approaches. While progress has been made, the robustness challenges of deep learning remain an important study vicinity that should be addressed so one can fulfill the promise of this generation. © 2024 IEEE.

关键词： Architectures Discovered Extraordinary Manipulating Researchers

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